Practical variable selection for generalized additive models
Practical variable selection for generalized additive models
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DOI：
10.1016/j.csda.2011.02.004
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发表时间：
2011-07-01
影响因子：
1.8
通讯作者：
Wood, Simon N.
中科院分区：
文献类型：
--
作者：
Marra, Giampiero;Wood, Simon N.
考虑了一类广义可加模型中的变量选择问题，当有许多协变量可供选择，但预测变量的数目仍略小于观测变量的数目时.介绍了两种非常简单但有效的收缩方法和非负Garrote估计的推广。这些建议避免了使用非参数检验方法，因为没有一般可靠的分布理论。此外，组件选择是在一个单一的步骤，而不是许多选择程序，涉及所有可能的模型的详尽搜索。所提出的方法的经验性能相比，一些可用的技术，通过广泛的模拟研究。结果表明，在何种条件下，一种方法可以优于另一种，从而提供了一些实用的指导原则，应用研究人员。该程序还说明了分析数据的血浆β-胡萝卜素水平从一个横截面的研究在美国进行。(C)2011 Elsevier B. V.保留所有权利。
The problem of variable selection within the class of generalized additive models, when there are many covariates to choose from but the number of predictors is still somewhat smaller than the number of observations, is considered. Two very simple but effective shrinkage methods and an extension of the nonnegative garrote estimator are introduced. The proposals avoid having to use nonparametric testing methods for which there is no general reliable distributional theory. Moreover, component selection is carried out in one single step as opposed to many selection procedures which involve an exhaustive search of all possible models. The empirical performance of the proposed methods is compared to that of some available techniques via an extensive simulation study. The results show under which conditions one method can be preferred over another, hence providing applied researchers with some practical guidelines. The procedures are also illustrated analysing data on plasma beta-carotene levels from a cross-sectional study conducted in the United States. (C) 2011 Elsevier B.V. All rights reserved.